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SECURITY INTELLIGENCE FABRIC · SIGNAL CORRELATION · LIVE ATTACK GRAPH

Correlate EDR, IAM, and cloudsignals into a singlelive attack graph.

Spakto's Security Intelligence Fabric ingests telemetry from every layer of your environment — endpoints, identity, cloud, network — and fuses it into one continuously updated attack graph that shows exactly what attackers see.

Security Intelligence Fabric · Live

Your tools see fragments.
Spakto sees the attack.

EDR sees malicious process execution. IAM sees a suspicious login. The cloud sees a privilege escalation. Individually, each looks like noise. Connected, they form the kill chain. The Security Intelligence Fabric makes those connections — automatically, in real time.

50+Signal Sources
<2sCorrelation Latency
99.7%Schema Coverage
ZeroSignal Silos
SIF · LIVE TOPOLOGY · SIGNAL INGESTION ACTIVE
ENDPOINTIDENTITYCLOUDNETWORK
EDREDRCrowdStrike·S1IAMIAMOkta·AzureADCLOUDCLOUDAWS·Azure·GCPNETWORKNETWORKPaloAlto·ZscalerSIEMSIEMSplunk·ElasticSECURITYINTELLIGENCEFABRICATTACK GRAPHLive topologyCORRELATION ENGINECross-domainAI DECISION LAYERRisk + intent4,247 events/s · 38ms P99 correlation · 3 active attack chains · 99.97% schema coverage
4,247/s
Events Ingested
38ms
P99 Correlation
3
Active Attack Chains
99.97%
Schema Coverage
Signal Fusion Pipeline · Streaming

From raw telemetry
to attack intelligence

Every signal passes through a 4-stage fusion pipeline before entering the live attack graph. Each stage runs as a streaming transformation — zero batch latency, continuous output.

SIF FUSION PIPELINE · STREAMING · 4,247 events/s throughput
INGEST50+ sourcesSTAGE 01NORMALIZEUniversal schemaSTAGE 02CORRELATEGraph traversalSTAGE 03ENRICHMITRE · AISTAGE 04RAWATTACKGRAPH
01 · INGEST4,247 events/s

Raw Signal Ingestion

  • Native connectors for 50+ platforms
  • Streaming ingestion via API, webhook, syslog
  • Automatic backpressure management
  • End-to-end encryption in transit
02 · NORMALIZE4,244 events/s

Schema Normalization

  • Vendor field mapping to universal schema
  • Type coercion and timestamp normalization
  • Deduplication within 500ms windows
  • Missing field inference and imputation
03 · CORRELATE12.3 chains/min

Cross-domain Correlation

  • Entity resolution across signal boundaries
  • Session stitching for same-actor events
  • Graph join: host → identity → cloud
  • MITRE ATT&CK technique tagging
04 · ENRICH3 attack paths active

AI-Powered Enrichment

  • Attacker intent classification
  • Blast radius and impact scoring
  • Crown jewel proximity calculation
  • Response playbook assignment
Signal Sources · Integration Registry

Every layer of your
environment, unified

Spakto integrates 50+ security tools across endpoint, identity, cloud, network, and DevOps platforms — normalizing signals into a single correlated schema.

50+
Native integrations
across 6 categories
EDR & Endpoint
12integrations
18ms
avg latency
Throughput87%
CrowdStrikeSentinelOneDefenderCarbon BlackCylance+7 more
·Process execution events
·Network connection logs
·File system activity
·Registry changes
·Memory injections
Identity & IAM
9integrations
23ms
avg latency
Throughput62%
OktaAzure ADGoogle WorkspacePing IdentityCyberArk+4 more
·Auth & MFA events
·Role and permission changes
·Session token activity
·OAuth grant logs
·Privileged access events
Cloud Platforms
8integrations
31ms
avg latency
Throughput74%
AWS CloudTrailAzure MonitorGCP AuditOracle CloudAlibaba+3 more
·API calls & responses
·IAM policy changes
·Resource creation events
·Data access logs
·Network egress records
Network & Firewall
11integrations
12ms
avg latency
Throughput93%
Palo AltoZscalerCisco FTDFortinetCheck Point+6 more
·NetFlow records
·DNS query logs
·Firewall allow/block
·VPN access events
·Proxy traffic logs
SIEM & Log Platforms
7integrations
41ms
avg latency
Throughput55%
SplunkElasticSentinelChronicleSumo LogicDatadog
·Correlation rule outputs
·Raw log streams
·Dashboard alert exports
·Custom query results
·Saved search outputs
Threat Intelligence
6integrations
58ms
avg latency
Throughput41%
MITRE ATT&CKRecorded FutureTenableVirusTotalISAC feedsCustom feeds
·Indicators of compromise
·TTP enrichment
·CVE & vuln data
·Dark web intel
·Leaked credential feeds
Cross-domain Correlation Engine

Isolated signals.
One attack chain.

Three tools see the same attack — none of them connect the dots alone. The Correlation Engine joins them in under 2 seconds, surfacing the full kill chain automatically.

SIF · CORRELATION ENGINE · ACTIVE CHAIN · ATTACK-2024-0847CRITICAL · 94.2% CONFIDENCE · 43s TO CORRELATE
EDR · WORKSTATION-1414:32:01Zsvchost.exe process in…MED14:32:15ZMimikatz hash dump att…HIGH14:33:02ZLSASS memory access de…HIGHIAM · svc-deploy@corp.com14:32:18ZAuth from new IP 185.2…MED14:32:44ZAssumeRole: prod-admin…CRIT14:33:10ZMFA bypass via legacy …HIGHCLOUD · AWS us-east-114:32:46ZS3:GetObject on prd-se…HIGH14:33:15ZEC2:RunInstances in pr…CRIT14:33:44ZCloudTrail logging dis…CRITCORRELATIONENGINE43s · 94.2% confATTACK CHAINATTACK-2024-0847 · CRITICAL
EndpointIdentity<1.2s

EDR host alerts joined with IAM activity, session tokens, and authenticated users on the same machine

IdentityCloud<1.8s

Suspicious IAM events linked to downstream AWS/Azure/GCP API calls, role assumptions, and data access chains

NetworkEndpoint<0.9s

NetFlow anomalies correlated with endpoint process trees — C2 and lateral movement detected before crown jewels are reached

CloudData<2.1s

Cloud API events cross-referenced with storage access and egress volumes — exfiltration chains identified early

Intelligence Fabric vs SIEM

Not a replacement.
A layer above.

Your SIEM stores and searches logs. Spakto's Intelligence Fabric correlates those signals into attacker traversal paths. They work together — and together they are significantly more effective.

Capability
SIEM Only
SIEM + Spakto Fabric ★
No Fabric
Signal correlation
Rule-based rules
Graph-based attack paths
Manual analyst work
Cross-domain visibility
Limited (single tool)
Full endpoint→IAM→cloud chain
Siloed per tool
Alert fidelity
10,000 alerts/day
3–5 attack chains/day
Alert fatigue
Attacker intent
No
AI-classified with confidence
No
Lateral movement detection
Partial
Real-time full traversal
Post-breach only
Time to correlate
Hours (manual)
<2 seconds (automated)
Days (manual)
Crown jewel proximity
No
Continuous blast-radius scoring
No
Kill chain completeness
Fragmented
Full MITRE kill chain mapped
Never complete
97%
Reduction in alert noise
43s
Average correlation time
Faster incident response
Use Cases · Who Relies on the Fabric

Built for every security
function in your org

From SOC analysts chasing threats to IR teams reconstructing breaches — the Intelligence Fabric is the shared substrate that makes every security function faster and more effective.

SOC · Threat Detection
97% alert reduction
from 10,000 alerts → 3-5 attack chains

Stop chasing individual alerts. The fabric correlates thousands of signals into a handful of high-fidelity attack chains — each with full context, attacker intent, and suggested response.

SIF-QLExample query
FABRIC.query({
  type: "active_attack_chains",
  min_confidence: 0.85,
  domains: ["endpoint","identity","cloud"],
  order_by: "severity DESC"
})
  • Real-time attack chain detection
  • Cross-domain signal correlation
  • MITRE ATT&CK technique mapping
  • AI-suggested response playbooks
IR · Incident Response
Minutes not days
to reconstruct full attack timeline

After a breach, trace backward through correlated signals to reconstruct the full attack chain — patient zero, every pivot, all affected assets — using the fabric&apos;s complete signal history.

SIF-QLExample query
FABRIC.timeline({
  entity: "svc-deploy@corp.com",
  window: "72h",
  include: ["predecessors","successors"],
  format: "kill_chain"
})
  • Complete signal audit trail
  • Entity-centric timeline rebuild
  • Patient-zero identification
  • Full blast radius mapping
Threat Hunting · Research
1 query = all domains
unified signal graph, no tool switching

Query the unified signal graph for attacker behaviors spanning tool boundaries. The lateral movement your SIEM, EDR, and IAM each saw a piece of — but never connected.

SIF-QLExample query
FABRIC.traverse({
  pattern: "T1078 -> T1021 -> T1078.004",
  timeframe: "last_7d",
  pivot_on: "identity",
  crown_jewel_proximity: true
})
  • Cross-domain graph queries
  • MITRE pattern matching
  • Identity pivot hunting
  • Crown jewel proximity alerts
Signal Normalization Engine

One schema. Every source.

Before correlation can happen, raw signals from 50+ tools must be normalized to a single canonical schema. The normalization engine resolves field naming conflicts, type mismatches, timestamp skew, and vendor-specific quirks — in real time, at stream speed.

SIGNAL NORMALIZATION ENGINE · SIF-SNE · v3.4.1
NORMALIZING · 4.2K events/s
RAW INGESTmulti-sourcePARSEvendor schemaNORMALIZEcanonical mapENRICHentity + MITREEMITgraph + streamavg pipeline latency: 3.1ms · throughput ceiling: 50K events/s · zero data loss guarantee
Raw Vendor Fields (sample)
CrowdStrikeUserName
CORP\john.doe
Oktaactor.alternateId
john.doe@corp.com
AWSuserIdentity.arn
arn:aws:iam::123:user/john
SentinelOnesrcProcUser
corp/john.doe
Splunkuser
john.doe
Resolution Engine
entityresolve
5 sources → 1 canonical identity
Canonical Schema Output
actor.idUSR-0041
actor.emailjohn.doe@corp.com
actor.display_nameJohn Doe
actor.domainCORP
actor.sources[CS, Okta, AWS, S1, Splunk]
actor.entity_conf0.98
actor.mitre_tacticT1078 · Valid Account
50+
Supported Sources
340
Schema Fields
3.1ms
Pipeline Latency
99.4%
Entity Accuracy
Fabric Telemetry Stream

Live signal stream, always enriched.

Every ingested event is enriched with entity context, MITRE tactic tagging, blast-radius impact score, and graph node linkage — before it leaves the ingestion pipeline. No post-processing lag. No cold-path correlation delays.

TELEMETRY STREAM · FABRIC-TS · LIVE · 4,218 events/s
STREAMING
14:38:22.114CrowdStrikejohn.doePROC_INJECTHIGHT10558.4
14:38:22.302Oktajohn.doeAUTH_SUCCESSMEDT10786.1
14:38:22.589AWSsvc-deployASSUME_ROLECRITT15509.2
14:38:22.741PaloAlto10.4.2.51LATERAL_MOVEHIGHT10217.8
14:38:23.012SentinelOnesvc-deployNET_CONN_OUTMEDT10415.5
14:38:23.198SplunkadminPRIV_ESCCRITT14849.7
14:38:23.441GCPsvc-accountBUCKET_READLOWT15303.2
14:38:23.602AzureADjohn.doeMFA_BYPASSCRITT15569.1
14:38:23.889DefenderWKSTN-14CRED_DUMPCRITT10039.5
14:38:24.102Zscaler10.4.2.51DNS_QUERY_C2HIGHT10718.0
14:38:24.318CrowdStrikesvc-deployFILE_WRITEMEDT10596.4
14:38:24.501AWSprod-adminS3_PUT_ACLHIGHT15307.3
14:38:24.712OktaadminPOLICY_CHANGEHIGHT14847.9
14:38:24.899Fortinet10.4.2.51FW_BYPASSHIGHT15998.1
14:38:25.111TenableWKSTN-14VULN_EXPLOITCRITT12039.3
14:38:25.302SentinelOnejohn.doeREGISTRY_MODMEDT15475.8
14:38:25.488AWSsvc-deployIAM_MODIFYCRITT10989.0
14:38:25.701GCPadminIAM_BINDHIGHT15488.2
Enrichment Layers · Per Event
01
Entity Resolution

Map actor to canonical identity node (USR / SVC / HOST)

02
Context Injection

Attach host, network, cloud resource context from asset graph

03
MITRE Tagging

Map event type → tactic + technique via behavioral classifiers

04
Blast Score

Compute blast_radius_delta: impact if event is part of attack

05
Graph Node Link

Attach event to existing graph node or create new attack path

Stream Throughput
CrowdStrike1,840/s
AWS912/s
Okta441/s
PaloAlto318/s
Others707/s
4,218
Events/sec
<2ms
Enrichment lag
380/s
Graph updates
12/hr avg
Attack signals
Noise Suppression Layer

10,000 alerts become 3 attack chains.

Alert fatigue is an architecture problem. The Noise Suppression Layer collapses correlated signal clusters into high-fidelity attack paths using graph topology, behavioral fingerprinting, and temporal session stitching — without losing a single true positive.

NOISE SUPPRESSION LAYER · SIF-NSL · REDUCTION ACTIVE
99.97% reduction
Suppression Funnel · 24-hour window
Raw Signals10,847Deduplicated3,241Entity-Resolved891Behaviorally Clustered74Attack Paths33 attack chains · 99.97% noise eliminated · 0 true positives suppressed
Suppression Techniques
NSL-01Deduplication

Hash-based dedup of identical events within 30s sliding window. Eliminates duplicate agent reports and log duplicates.

window: 30s · hash: sha256(src+actor+ev+asset)
NSL-02Entity Clustering

Group signals sharing the same resolved entity (user, host, role). Collapses 100s of per-event alerts into entity-level sessions.

session_gap: 5min · merge_key: entity_id + domain
NSL-03Behavioral Fingerprinting

ML classifiers trained on known attacker behavioral patterns. Signals matching known false-positive patterns are suppressed.

model: gradient boost · precision: 99.8% · recall: 100%
NSL-04Temporal Stitching

Chain events within a session window into single attack sequences. 47 individual alerts become 1 lateral movement record.

chain_window: 15min · min_confidence: 0.72
Output: 3 High-Fidelity Attack Paths · from 10,847 raw signals
ATK-001CRITICAL
Lateral Movement to AWS
4,218
Signals
5
Hops
99.2%
Conf
T1078·T1550·T1484
ATK-002HIGH
Credential Dumping Chain
2,891
Signals
3
Hops
97.8%
Conf
T1003·T1055·T1021
ATK-003HIGH
Data Exfil via Cloud API
3,738
Signals
4
Hops
94.1%
Conf
T1530·T1537·T1041

Security Intelligence Fabric FAQs

Frequently asked
questions.

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